# Config Parameters

options = dict()
options["data_dir"] = "./data/"
options["window_size"] = 10
options["device"] = "cpu"

# Smaple
options["sample"] = "sliding_window"
options["window_size"] = 10  # if fix_window

# Features
options["sequentials"] = True
options["quantitatives"] = True
options["semantics"] = False
options["feature_num"] = sum(
    [options["sequentials"], options["quantitatives"], options["semantics"]]
)

# Model
options["input_size"] = 1
options["hidden_size"] = 64
options["num_layers"] = 2
options["num_classes"] = 133

# Train
options["batch_size"] = 1024
options["accumulation_step"] = 1

options["optimizer"] = "adam"
options["lr"] = 0.001
# options['max_epoch'] = 370
options["max_epoch"] = 300
options["lr_step"] = (300, 350)
options["lr_decay_ratio"] = 0.1

options["resume_path"] = None
options["model_name"] = "loganomaly"
options["save_dir"] = "./result/loganomaly/"

# Predict
options["model_path"] = "./result/loganomaly_last.pth"
options["num_candidates"] = 9
